Machine Learning Consulting Services 

The amount of data will depend directly on the complexity of the model. For simple models, like linear regression, a few thousand samples might be sufficient. An example of a use case for such a model would be the prediction of a hospital readmission with patient demographics.

For complex models, you might need tens of thousands (and up to millions) of samples so the model is trained effectively. An example of a use case would be an advanced cancer detection system trained on MRI scans. Note though that when it comes to model training, it’s better to have high-quality data rather than an overwhelming amount of noisy or biased data. 
In case an organization does not have labeled data (which is a very common case, especially in healthcare and fintech domains), there are several solutions:

1.Use semi-supervised learning: combine a small labeled dataset with a larger pool of unlabelled data and let the model learn from it, once it completes training with labeled data.
2. Deploy transfer learning: use a pre-trained model from a similar domain and fine-tune with your existing labeled dataset.
3. Augment your data: transform the existing data to artificially increase the dataset size. Examples of such transformations include image rotation, cropping, and brightness adjustment.
4. Use synthetic data: use Generative AI to generate artificial data and increase your existing dataset.

Of course. SoftTeco’s main goal is to help you decide on the machine learning solution that will bring measurable value to your business and will address the existing pain points. For that, it is critical to have high-quality labelled data and we will help you design a data strategy from the start.

The process includes such steps as audit of the existing data (its volume, velocity, etc.), establishment of the data ownership (i.e., who is responsible for the collection and labeling processes), and setup of data pipelines. We will also define the tech stack and the team and will outline a project roadmap so you have a clear understanding of the steps to take before starting machine learning development.

By default, ML models do not learn and improve over time as they are trained on specific data sets and cannot update themselves. Hence, if you want your model to remain relevant and accurate, you will need to retrain it consistently by using new data. You can even set up automated pipelines so the new data is fed to the model without human intervention.

You can also consider following reinforcement learning or active learning approaches. In this way, the model keeps learning from the feedback (reinforcement learning) or applies new labels to existing data (active learning), thus improving its own performance.

Yes, the use of ML won’t compromise your sensitive data – here is how SoftTeco ensures that:

1. Data de-identification & anonymization: we can remove personal identifiers (i.e., user names or addresses) from the dataset before starting the training process. We will also check that the model learns patterns without exposing user identities for better security.
2. Federated learning approach: we will train the model locally on your specific data.
3. Encrypted computation: if needed, we can train the model on encrypted data so the system cannot see raw data.

In addition, we will implement strict access control and audit logs so only authorized users can work with the data. 

SoftTeco is an ISO 9001 and ISO 27001 certified company, which indicates our multi-layered approach to data compliance and security. We establish a strong data governance policy and classify the data to ensure the sensitive information is not revealed. We also use such practices as data encryption at rest and in transit, log audits, and data de-identification. Depending on the industry and the countries where you operate, we will check that our procedures align with regulatory requirements like HIPAA, GDPR, or CCPA, and will ensure that local regulations are covered as well.